A Success Assessment Model for BI Tools Implementation: An Empirical Study of Banking Industry

A Success Assessment Model for BI Tools Implementation: An Empirical Study of Banking Industry

Saeed Rouhani (Faculty of Management, University of Tehran, Tehran, Iran) and Sogol Rabiee Savoji (Department of IT Engineering, MehrAlborz University, Tehran, Iran)
Copyright: © 2016 |Pages: 20
DOI: 10.4018/IJBIR.2016010103
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Abstract

In today's rapidly-changing business environment, the need for useful business analytics is vital for organizations, not only to succeed, but also to survive. Traditional enterprise systems have disabilities to meet the expectations of organizational decision makers in the competitive area. In this regard, it is necessary to evaluate the success of BI tools in organizations, and there is a need to provide a model for this assessment. Hence, in this study, a model for assessing the success of business intelligence is presented by identifying and introducing the most important and effective factors in evaluating the success of BI tools. This study is an applied study in terms of purpose and a survey-descriptive, empirical study in terms of methodology. According to statistical methods, importance of the success factors was evaluated and the results show that 24 factors were identified consequential in research model based on four areas such as organizational memory, information integration, knowledge creation, and presentation.
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1. Introduction

A major issue in many firms was the integration of data and processes from these heterogeneous systems and organizational diversity applications. This lack of integration resulted in data congestion, poor data quality, inconsistent data definitions and formats, disjointed and poorly defined business processes, and poor information access due to a diversity of user interface designs, lack of knowledge and inadequate reports. The lack of integration hindered business process execution and effective decision making (Hawking & Sellitto, 2010).

Business Intelligence (BI) solutions have made a top priority of IT executives in organizations who implemented these solutions for several years and the market for related software products continues growing rapidly (Ishikiriyama, Miro, & Gomes, 2015; Rouhani, Ashrafi, Zare, & Afshari, 2016; Wieder & Ossimitz, 2015). An early study by IDC (1996) found that companies that used BI effectively could achieve an average of 401 percent return on investment (ROI) over a three-year period. In a Cutter Consortium Report (2003) survey of 142 companies, it was found that 70 percent of the respondents had implemented data warehousing and BI initiatives (Herzum, 2003). Gartner (2009), a leading information technology research and advisory company, conducted a worldwide survey of 1,500 Chief Information Officers and identified the BI area as a number one technology priority. According to Gartner Inc., BI platforms allow companies to develop BI applications that offer three categories of functionalities: “analysis (OLAP1), information delivery (reports and dashboards), and platform integration (BI metadata management and a development environment)” (Tutunea, 2015).

Although BI has the potential to improve the performance of a company, a review of the literature indicated that a significant number of companies often failed to realize the expected benefits of BI and sometimes considered a BI project as a failure in itself (Chenoweth, Corral, & Demirkan, 2006; Hwang & Xu, 2005; Johnson, 2004). Gartner predicted that more than half of 2000 Global enterprises would fail to realize the capabilities of BI and would subsequently relinquish market share to companies that had leveraged BI (Dresner et al., 2002). A survey of 142 companies found that 41 percent of the respondents had experienced at least one BI project failure and only 15 percent of respondents believed that their BI initiative was a major success (Hawking & Sellitto, 2010).

However, considering the studies conducted, so far little has been done in order to study and explain dimensions and factors of success evaluation of these systems and no systematic studies have been carried out about the success evaluation of business intelligence tools after implementation with a focus on banking area and their application scope. Moreover, it is essential to evaluate the success of these systems in organizations and it is highly important to present a model to deal with the evaluation. Thus, this study attempts to identify and study the factors affecting the efficacy measurement and business intelligence success tools after implementation in banks as one of the best organizations to implement the system due to the sensitivity of the data in the market competitive atmosphere and the wide variety of available information systems.

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